A critical driving force behind research in bioprocess science and engineering continues to be the demand for fast and accurate analytical information that can be used, for example, to evaluate the interactions between biological systems and bioprocess operations. One significant challenge is to carry out large numbers of experiments rapidly and efficiently. This issue is of particular importance since many of the advances in molecular biology now lead to large numbers of potential biological systems that contain evolved biocatalysts, new pathway designs, and a variety of unique biological organisms from diverse sources.
Bioprocess development techniques have been unable to keep pace with the current rate of discovery and genetic manipulation in biological systems. Of the hundreds of thousands of genetic and process permutations that can now be designed, only a small fraction can be tested using standard bioprocess practices. Bench-scale bioreactors, with typical volumes of between 2 and 10 liters, are limiting for a number of reasons including the time required to obtain sufficient data for a biological system, the effort required to obtain the data, and the high cost of these systems. Currently the smallest bioreactors that are available commercially have working volumes of approximately 0.5 liters (Sixfors, Appropriate Technical Resources) and allow six parallel fermentations to be carried out.
There exists a need for systems that allow rapid testing, process development, and optimization to be carried out through parallel fermentations. In particular, there exists a need for microscale bioreactor systems that allow multiple experiments to be performed in parallel without an accompanying increase in cost. In addition, there exists a need for microscale bioreactor systems wherein experimental conditions and results obtained in the microscale bioreactor may be translated into predictable large-scale bioprocess operations.
The above needs are not limited to bioprocess development but extend more generally to other settings, e.g., any settings in which it is desired to test or optimize reaction conditions, substrates, etc.